An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, text analytics, cybersecurity, and many more. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. This article presents a structured and comprehensive view on DL techniques including a taxonomy considering various types of real-world tasks like supervised or unsupervised. In our taxonomy, we take into account deep networks for supervised or discriminative learning, unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with research directions. Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals.

/pdf/deep-learning-a-comprehensive-overview-on-techniques-1wh2eij623.pdf

Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions.

In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss on cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.

/pdf/cybersecurity-data-science-an-overview-from-machine-learning-2g5zdq7ti3.pdf

Cybersecurity data science: an overview from machine learning perspective

Artificial intelligence (AI) is one of the key technologies of the Fourth Industrial Revolution (or Industry 4.0), which can be used for the protection of Internet-connected systems from cyber threats, attacks, damage, or unauthorized access. To intelligently solve today’s various cybersecurity issues, popular AI techniques involving machine learning and deep learning methods, the concept of natural language processing, knowledge representation and reasoning, as well as the concept of knowledge or rule-based expert systems modeling can be used. Based on these AI methods, in this paper, we present a comprehensive view on “AI-driven Cybersecurity” that can play an important role for intelligent cybersecurity services and management. The security intelligence modeling based on such AI methods can make the cybersecurity computing process automated and intelligent than the conventional security systems. We also highlight several research directions within the scope of our study, which can help researchers do future research in the area. Overall, this paper’s ultimate objective is to serve as a reference point and guidelines for cybersecurity researchers as well as industry professionals in the area, especially from an intelligent computing or AI-based technical point of view.

https://link.springer.com/content/pdf/10.1007/s42979-021-00557-0.pdf

AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model.

IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model

This paper describes a focused literature survey of self-organizing maps (SOM) in support of intrusion detection. Specifically, the SOM architecture can be divided into two categories, i.e., static-layered architectures and dynamic-layered architectures. The former one, Hierarchical Self-Organizing Maps (HSOM), can effectively reduce the computational overheads and efficiently represent the hierarchy of data. The latter one, Growing Hierarchical Self-Organizing Maps (GHSOM), is quite effective for online intrusion detection with low computing latency, dynamic self-adaptability, and self-learning. The ultimate goal of SOM architecture is to accurately represent the topological relationship of data to identify any anomalous attack. The overall goal of this survey is to comprehensively compare the primitive components and properties of SOM-based intrusion detection. By comparing with the two SOM-based intrusion detection systems, we can clearly understand the existing challenges of SOM-based intrusion detection systems and indicate the future research directions.

A Survey on the Development of Self-Organizing Maps for Unsupervised Intrusion Detection

In this paper, we proposed a new collaboration-based transmission scheme for overlay cognitive full-duplex wireless wiretap networks, where a full-duplex transmitter can help the primary system transmit information and utilizes the licensed spectrum to transmit its own information. In order to analyze the performance of the proposed scheme, we derive the exact secrecy outage probability expression of primary information transmission and the effective throughput of secondary information transmission. From the simulation results, we can conclude that the proposed scheme can effectively improve the secrecy performance of primary transmission and throughput of secondary transmission without any other additional power.

Secrecy and Efficiency in Overlay Cognitive Full-duplex Wireless Networks

In this paper, a robust power management method is proposed to solve the power allocation problem in a jammed cooperative wireless localization system. Because the localization accuracy and the lifetime of networks are both depended on the power allocation strategies, it is a critical task to trade-off the localization accuracy and power consumption through power management method. Moreover, if there are jammer nodes, the problem meets new challenge to consider the effect of jammers. So in this paper, we develop a power management method to minimize the localization errors with power constraints. First, the cooperation between agent nodes is considered in a jammed wireless localization system. Then the uncertain parameters of different nodes are derived to obtain robust power allocation strategies. What’s more, the problem can be transformed into semi-definite programs (SDPs) to have efficient solutions. Simulation results indicate that the proposed power management method can reach more efficient power allocation strategies than uniform power allocation and the robust strategies have better performance than non-robust strategies.

Robust Power Management for Cooperation in Jammed Wireless Localization Systems

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Papers

A Survey on the Development of Self-Organizing Maps for Unsupervised Intrusion Detection

Secrecy and Efficiency in Overlay Cognitive Full-duplex Wireless Networks

Robust Power Management for Cooperation in Jammed Wireless Localization Systems